To include reviews, specs, or tags in a product embedding, you need to process and combine these data types into a single numerical vector that represents the product. Start by converting each data type into its own embedding using appropriate techniques, then merge them into a unified representation. For example, text-based data like reviews can be encoded with language models, specs can be structured into numerical or categorical features, and tags can be treated as sparse vectors or embeddings. The key is to ensure all inputs are transformed into compatible formats and combined in a way that preserves their semantic meaning.
First, process each data type separately. For reviews, use a text embedding model like BERT or a sentence transformer to convert raw text into fixed-length vectors. These models capture semantic meaning and context, which helps represent reviews effectively. Specs, such as product dimensions or technical details, can be handled as structured data. Normalize numerical values (e.g., scaling screen sizes from 0 to 1) and encode categorical values (e.g., “material: plastic” as one-hot vectors). Tags, which are often keywords like “waterproof” or “wireless,” can be embedded using techniques like TF-IDF, word2vec, or even simple binary encoding (presence/absence of a tag). Each method has trade-offs: binary encoding is lightweight but loses semantic relationships, while word2vec captures tag similarities.
Next, combine the embeddings. A common approach is concatenation: stack the review embedding vector, specs vector, and tags vector into a single long vector. For instance, if reviews produce a 768-dimensional vector (from BERT), specs add 10 normalized features, and tags contribute 50 binary flags, the final embedding would be 828 dimensions. Alternatively, use weighted averaging or attention mechanisms to prioritize certain features—like giving reviews higher weight if user sentiment is critical for your use case. Ensure all components are scaled appropriately to prevent one data type from dominating the embedding. For example, apply L2 normalization to each subset of features before merging.
Finally, validate and refine the embedding. Test the combined vector’s performance in downstream tasks like search, recommendation, or classification. If a product search system uses this embedding, evaluate whether query results align with user expectations. Adjust the processing steps based on results: for example, if tags aren’t improving accuracy, try a different encoding method or exclude them. Tools like PCA or t-SNE can help visualize the embedding space to check for logical clustering (e.g., similar products grouped together). Keep computational efficiency in mind—large embeddings may require dimensionality reduction techniques (e.g., autoencoders) for real-time applications. By iteratively refining how each data type is processed and merged, you can create a product embedding that effectively leverages reviews, specs, and tags.